Drinking patterns, alcohol-related harm and views on policies: results from a pilot of the International Alcohol Control Study in Canada
Bibliographic record
Abstract
INTRODUCTION: We conducted a pilot assessment of the feasibility of implementing the International Alcohol Control (IAC) Study in Ontario, Canada, to allow for future comparisons on the impacts of alcohol control policies with a number of countries. METHODS: The IAC Study questionnaire was adapted for use in the province of Ontario, and a split-sample approach was used to collect data. Data were collected by computer-assisted telephone interviewing of 500 participants, with half the sample each answering a subset of the adapted IAC Study survey. RESULTS: Just over half of the sample (53.6%) reported high frequency drinking (once a week or more frequently), while 6.5% reported heavy typical occasion drinking (8 drinks or more per session). Self-reported rates of alcohol-related harms from one's own and others' drinking were relatively low. Attitudes towards alcohol control varied. A substantial majority supported more police spot checks to detect drinking and driving, while restrictions on the number of alcohol outlets and increases in the price of alcohol were generally opposed. CONCLUSION: This pilot study demonstrated that the IAC Study survey can be implemented in Canada with some modifications. Future research should assess how to improve participation rates and the feasibility of implementing the longitudinal aspect of the IAC Study. This survey provides additional insight into alcohol-related behaviours and attitudes towards alcohol control policies, which can be used to develop appropriate public health responses in the Canadian context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".